- The following is a conversation with Jensen Huang,

CEO of NVIDIA, one of the most important and influential

companies in the history of human civilization.

NVIDIA is the engine powering the AI revolution, and a lot

of its success can be directly attributed to Jensen's sheer force of

will and his many brilliant bets and decisions as a leader, engineer, and innovator.

This is Lex Fridman Podcast. And now dear friends, here's Jensen Huang.

You've propelled NVIDIA into a new era in

AI, moving beyond its focus on chip scale design to now rack scale

design. And I think it's fair to say that winning for NVIDIA for a

long time used to be about building the best GPU possible, and you still

do, but now you've expanded that to extreme co-design of

GPU, CPU memory, networking, storage, power cooling,

software, the rack itself, the pod that you've announced, and even the

data center. So let's talk about extreme co-design. What is the

hardest part of co-designing system with that many complex

components and design variables?

- Yeah, thanks for that question.

So first of all, the reason why extreme co-design is necessary is because the

problem no longer fits inside one computer to be accelerated by one GPU.

The problem that you're trying to solve is you would like to go faster

than the number of computers that you add. So you added

you know, 10,000 computers, but you would like it to go a million times

faster. Then all of a sudden you have to take the algorithm,

you have to break up the algorithm, you have to refactor it,

you have to shard the pipeline, you have to shard the data, you have to shard the

model. Now all of a sudden when you distribute the problem this way,

not just scaling up the problem, but you're distributing the problem,

then everything gets in the way. This is the Amdahl's Law

problem where the amount of speed up you have for something

depends on how much of the total workload it is. And so

if computation represents 50% of the problem, and I sped up computation infinitely

like a million times,

you know, I only sped up the total workload by a factor of two.

Now all of a sudden, not only do you have to distribute a

computation, you have to, you know, shard the pipeline somehow.

You also have to solve the networking problem

because you've got all of these computers are all connected together.

And so distributed computing at the scale that we do,

the CPU is a problem, the GPU is a problem, the networking is a problem, the

switching is a problem. And distributing the workload across all

these computers is a problem.

It's just a massively complex computer science problem. And so we just

gotta bring every technology to bear. Otherwise, we scale up linearly or we

scale up based on the capabilities of Moore's Law, which has

largely slowed because Dennard scaling has slowed.

- I'm sure there's trade-offs there. Plus you have a complete disparate

disciplines here. I'm sure you have specialists in each one of these high bandwidth

memory, the network and the NVLink, the NICs, the optics and the

copper that you're doing, the power delivery, the cooling, all of that. I mean, there's like world

experts in each of those. How do you get 'em in a room together to figure out-

- That's why my staff is so large.

- What's the process—can you take me through the process of the specialists and the

generalists? Like how do you put together the rack when you know the

s- the set of things you have to shove into a rack together?

Like what does that process look like of designing it all together?

- Yeah. There's the first question, which is: what is extreme co-design?

You're, you, we're optimizing across the entire stack of software

from architectures to chips, to systems, to system software, to the

algorithms, to the applications. That's one layer. The second thing that you and

I just talked about is goes beyond CPUs and GPUs and

networking chips and scale up switches and scale out switches.

And then of course, you gotta include power and cooling and all

of that because, you know, all these computers are extremely,

extremely power hungry. They do a lot of work and they're very

energy efficient, but they in aggregate still consume a lot of

power. And so that's one. The first question is, what is it?

The second question is, why is it, and we just spoke about the reason, you

know you want to distribute the workload so that you can exceed

the benefit of just increasing the number of computers.

And then the third question is, how is it, how do you do it?

And that's the, that's kind of the miracle of this

company. You know, when you're designing a computer, you have to have operating system of

computers. When you're designing a company,

you should first think about what is it that you want the company to produce. You know, I see

a lot of companies organization charts, and they all look the same.

Hamburger organization charts, soft organization charts, and car

company organization charts. They all look the same.

And it doesn't make any sense to me. You know, the goal of a company is to be the

company is to be the machinery, the mechanism, the system that produces

the output. And that output is the product that we like to create.

It is also designed, the architecture of the company should reflect

the environment by which it exists. It almost indirectly

says what you should do with the organization. My direct staff is 60 people.

You know, I don't have one-on-ones with 'em because it's impossible. You can't have, you can't have 60 people

on your staff if you're, you know, gonna get work done and-

- So you still have 60 reports. You still have across-

- More, yeah.

- More. And most stars at least have a foot in engineering.

- Almost all of them.

There's experts in memory, there's experts in CPUs, there's experts in

optical. All, all—

- That's incredible.

- Yeah, GPUs and— Architecture, algorithms, design, um—

- So, you constantly have an eye on the entire stack, and you're having to, like,

intense discussions about the designer of the entire stack?

- And no conversation is ever one person. That's why I don't do

one-on-ones. We present a problem

and all of us attack it. You know, because we're doing extreme co-design.

And literally, the company is doing extreme co-design all the time.

- So, even if you're talking about a particular component, like cooling,

networking, everybody's listening in?

- Yeah, exactly.

- And they can contribute, "Well, this doesn't work for the power distribution.

This doesn't-"

- Exactly.

- "... This doesn't work for the memory. This doesn't work for this."

- Exactly. And whoever wants to tune out, tune out. You know what I'm saying?

And the reason for that is because the people who are on the staff, they know

when to pay attention. There's supposed... You know, it's something they could have

contributed to, they didn't contribute to, "I'm going to call them out." You know?

And so, "Hey, come on, let's get in here."

- So, as you mentioned, NVIDIA is this company that's adapting to the environment.

So, at which point can you say, did the environment change and

began adapting sort of secretly-

... in the early days from GPU for gaming, maybe the

early deep learning revolution to we're now going to start thinking of it as an AI

factory? What does NVIDIA do? It produces AI, let's build a factory that makes AI.

- Uh, I could, I c- you, you could- I could reason through what just systematically.

We started out as a, as an accelerator company.

But the problem with accelerators is that the application domain's too narrow.

It has the benefit of being incredibly optimized for the

job. You know, any specialist has that benefit. The problem with

intense specialization is that, of course, your market reach is narrower,

but that's, that's even fine. The problem is, the market size also

dictates your R&D capacity. And your R&D capacity ultimately dictates

the influence and impact that you can possibly have in computing. And so,

when we first started out as an accelerator, very specific accelerator,

we always knew that had- that was going to be our first step. We had to find a

way to become accelerated computing. But the problem is, when you become a

computing company,

it's too general purpose and it takes away from your specialization. The

tur- I connected two words that are actually

have fundamental tension. The better computing company we become, the

worse we became as a specialist. The more of a specialist, the less

capacity we have to do overall computing. And so,

that... And I connected those two words together on purpose, that the company

has to find that really narrow path, step by step by step, to expand our

aperture of computing, but not give up on the most important

specialization that we had. Okay, so the first step that we took beyond

acceleration was, we invented a programmable pixel shader.

So, that was the first step towards programmability. You know, it was our

first journey towards moving into the world of computing. The second thing that we

did was we created we put

FP32 into our shaders. That FP32 step, IEEE-compatible FP32, was a huge step

in the direction of computing. It was the reason why all of the people who were

working on, on stream processors and, you know,

other types of data flow processors discovered us. And they said, "Hey, all of

a sudden, you know, we might be able to use this GPU that's incredibly computationally

intensive, and it's now, you know, compliant with IEEE."

I can take my software that I was writing, you know, previously on

CPUs, and I can, you know, see about, you know, using the GPU for that.

And which led us to create, put C on top of

FP32, what's called, we call Cg. The Cg path

took us to eventually CUDA. CUDA, step by step by step

We... Well, putting CUDA on GeForce, that was

a strategic decision that was very, very hard to do, because it cost the

company enormous amounts of our profits, and we couldn't afford it

at the time. But we did it anyways because we wanted to be a computing

company. A computing company has a computing

architecture. A computing architecture has to be compatible across all of

the chips that we build.

- Can you take me through that decision? So, putting CUDA on GeForce, could not afford to

do? Can you explain that decision? Why, why boldly choose to do that anyway?

Can you explain that decision?

- Yeah, excellent. That was, that was the first... I would say that that was

the first strategic decision that is as close to an existential threat.

- For people who don't know, it turned out to be, spoiler alert,

one of the most incredibly brilliant decisions ever made

by a company. So, CUDA turned out to be

an incredible foundation for computation in this AI infrastructure world. So-

- Thank you

- ... just setting the context. It turned out to be a good decision.

- Yeah, it turned out to have been a good decision. I think the... So, here's the way it

went. So, we invented this thing called CUDA, and

It expanded the aperture of applications

that, that we can accelerate with our accelerator. The question is, how do we,

how do we attract developers to CUDA?

Because a computing platform is all about developers. And developers

don't come to a computing platform just because, you know, it

could perform something interesting. They come to a computing platform because the

install base is large.

Because a developer, like anybody else, wants to develop software that

reaches a lot of people. So, the install base is, in fact,

the single most important part of an architecture. The

architecture could attract enormous amounts of criticism. For example,

no architecture has ever attracted more criticism than the

x86.... you know, as a less than, less than elegant architecture, but yet it is the

defining architecture of today. It gives you an example that in fact

so many RISC architectures which were beautifully architected,

incredibly well-designed by some of the brightest computer scientists in the

world, largely failed. And so I've given you two

examples where one is, you know, one is elegant, the other one's

barely aesthetic, and so yet x86 survived and the reason for-

- Install base is everything.

- Install base defines an architecture. Not... Everything else

is secondary, okay? And so there were other architectures at the time.

CUDA came out, OpenCL was here. There were... You know, there's several other competing

architectures. But the thing that... The decision that we made that was good

was we said, "Hey, look, ultimately it's about,

Install base and what is the best way we could get a new

computing architecture into the world?"

By that timeframe, GeForce had become successful. We were already

selling millions and millions of GeForce GPUs a year, and we

said, "You know, we ought to put CUDA on GeForce

and put it into every single PC whether customers use it or not,

and use it as a starting point of cultivating our install base."

Meanwhile, we'll go and attract developers, and

we went to universities and wrote books and taught

classes and put CUDA everywhere. And eventually people discover...

And at the time, the PC was the primary computing vehicle. There was no

cloud, and we could put a supercomputer in the hands of every

researcher in school, every scientist, you know, every engineering school, every...

or every student in school, and eventually something amazing will happen.

Well, the problem was CUDA increased our cost

of that GPU, which is a consumer product, so tremendously,

it completely consumed all of the company's gross profit dollars.

And so at the time, the company was probably, you know, worth, I don't know, at the time,

eight...

Was it like $8 billion or something? Like six, $7 billion or something like that.

After we launched CUDA, I recognized that it was going to add so much cost,

but it was something we believed in.

You know, our market cap went down to like one and a half billion dollars. And so

we were down, we were down there for a while and we

clawed our way way back slowly, but we carried

CUDA on GeForce. I always say that NVIDIA is the house that

GeForce built, because it was GeForce that took CUDA out to

everybody. Researchers, scientists, they discovered CUDA on GeForce because they were

all, you know... Many of 'em were gamers. Many of them built their own

PCs anyways. In a university lab, many of them built

clusters themselves, you know, using PC components. And,

and so that, you know, that's kind of how we got going.

- And then that became the platform and the foundation for the deep learning revolution.

- That was also another great, great observation. Yeah.

- That existential moment, do you remember... Like, what were those meetings

like? What were those discussions like, deciding as a company, risking everything?

- Well I had to make it clear to the board what we're trying to do, and

the management team knew our gross margins were gonna get crushed.

So you could imagine a world where GeForce would carry

the burden of CUDA and none of the gamers would

appreciate it and none of the gamers would pay for it. You know, they only pay

certain price and it doesn't matter what your cost is. And so the... You know,

we, we increased our cost by 50% and that con-

consumed... And we were a 35% gross margin company, and so it,

it was a... It was quite a difficult decision to make. But

you could imagine that someday this would go into workstations and it would go into

supercomputers and, and in those segments, maybe we can capture more margin.

so you could, you could reason your way into being able to afford this,

But it still took... It took a decade.

- But that, but that's more of, like, conversation with the board convincing them, but you

psychologically- ... as NVIDIA's continued to make bold bets

that predict the future, and in part, especially now, define the future.

So I'm almost looking for wisdom about

how you're able to make those decisions, to make leaps- ... like that as a company.

- Well, first of all I'm informed by a lot of curiosity.

At some point, there's a reasoning system that,

that convinces me, so clearly this outcome will happen. That this will happen.

And so I believe it in my mind, and when I believe it in my mind,

you know, you know how it is. You manifest a future and that

future is so convincing, there's no way it won't happen.

There's a lot of suffering in between, but you've gotta believe what you believe.

- So you envision the future-

... and you essentially, from a sort of engineering perspective, manifest it?

- Yeah. And you, you reason about how to get there. You reason about why

it, it must exist. And, you know, I reason... We all

reason it here. The management team would reason about it. All the people that I...

We spend a lot of time reasoning about it. The thing, the thing that... The next

part of it is probably a skill thing, which is,

you know, oftentimes in leadership the leadership

stays quiet or they learn about something, and then they do some manifesto,

and it's a brand-new year, and somehow at the end of the year, next year,

we're gonna have a brand-new plan. Big huge layoff this way, big huge

organization change this way, new mission

statement... brand new logos you know, that kind of stuff.

Um, we've just never, I never do things that way. When I learn about something

and it's starting to influence how I think, I'll make it very clear to

everybody near me that, you know, this, this is interesting.

This is going to make a difference. This is going to impact that.

And I reason about things step by step by step. Oftentimes, I've already made up

my mind, but I'll take every possible opportunity,

external information, new insights, new discoveries,

New engineering, you know, revelations, new

milestones developed, I'll take those opportunities and I'll use

it to shape everybody else's

belief system. And I'm doing that literally every single day.

I'm doing that with my board, I'm doing that with my management team, I'm doing that with

my employees. I'm trying to shape

their belief systems such that when I come the day I say, "Hey, let's buy Mellanox,"

it's completely obvious to everybody that we absolutely should.

On the day that I said, "Hey guys, let's

go all in on deep learning," and let me tell you why. I've

already been laying down the bricks to different organizations

inside the company. Every organization and everybody,

many of the people might have heard everything.

Most of the company hears, of course, pieces of it.

And on the day that I announce it,

everybody's kind of bought in to many pieces of it.

And in a lot of ways, I like to announce these things, and I imagine,

that the employees are kind of saying, "You know, Jensen, what took you so long?"

And in fact, I've been shaping their belief system for some time, and therefore

leadership. Sometimes it looks like you're leading from behind,

but you've been shaping their, you know, to the point where on the day that I declared it,

100% buy-in. But that's what you want. You want to bring everybody

along. You know, otherwise, we announce something about deep learning and everybody goes, "What

are you talking about?" You know, you announce something about let's go all in

on this thing, and your management team, your

board, your employees, your customers, they're kind of like, "Where's this coming from?

You know, this is insane." And so, so GTC

effect, if you go back in time, you look at the keynotes,

I'm also shaping the belief system of my partners in the

industry and I'm using that to shape, you know, the belief

system of my own employees. And so by the time that I announce something,

like for example, we just announced Grok.

We've been late... I've been talking about the stepping stones for two and a half

years.

You just go back and go, "Oh my gosh, they've been talking about it for two and a

half years." And so I've been laying the foundation step by step by step,

so when the time comes you announce it, everybody's saying, "You know, what took you so long?"

- But it's not just inside the company. You're shaping the landscape, the broader global landscape

of innovation. Like, putting those ideas out there, you really are manifesting

reality.

- We don't build computers. We actually don't build clouds. We don't... As

it turns out, we're a computing platform company. And so nobody can

buy anything from us. That's the weird thing. You know, we vertically

design, vertically integrate to design and optimize, but then

we open up the entire platform at every single layer

to be integrated into other companies' products and services and

clouds and supercomputers and OEM computers

and so the amazing thing is, I can't do what I

do without having convinced them first. And so most of

GTC is about manifesting a future that by the

time that we... My product is ready, they're going, "What took you so long?"

- Yeah. So one of the things you've been a believer for a long

time is scaling laws, broadly defined. So are

you still a believer in the scaling laws?

- Yeah, yeah. Yeah, we have more scaling laws now.

- So I think you've outlined four of them with pre-training, post-training, test

time, and agentic scaling. What do you think, when you think about the future,

deep future and the near-term future, what are the blockers

that you're most concerned about that keep you up at night that you have to overcome

in order to keep scaling?

- Well, we can go back and reflect on what people thought were blockers.

So in the beginning, we were the first... The pre-training scaling

law. You know, people thought well rightfully

so, that the amount of data that we have, high-quality data that we

have will limit the intelligence that we achieve. And that scaling law

was an important, very important scaling law. The larger the model, the

correspondingly more data results in a better... With a

results in a smarter AI. And so that was pre-training. And

Ilya Sutskever, Ilya said, "We're out of data," or something like that. "Pre-training is

over," or something like that. The industry panicked,

you know, that this is the end of AI. And of course, of course that, that's

obviously not true. We're gonna keep on scaling the amount of data that we

have to, to train with. A lot of that data is probably gonna be synthetic,

and that also confused people, you know? And what people don't

realize is they've kind of forgotten that most of the data

that, that we are training that we teach each other with, inform

each other with, is synthetic. You know, I...

It's synthetic because it didn't come out of nature.

You created it. I'm consuming it. I modify it, augment it,

I regenerate it, somebody else consumes it. And so, so

we've now reached a level where AI is able to take ground truth, augment it......

Enhance it, synthetically generate an enormous amount of

data. And that part of post-training

continues to scale, and so the amount of data that we could use

that is human generated will be smaller, and smaller, and smaller.

The amount of data that we use to train model,

is going to continue to scale to the point where we're no longer limited...

Training is no longer limited by... Data is now limited by compute. And the

reason for that is most of the data is synthetic. Then the next phase

is test time, and

I still remember people telling me that, "Inference? Oh, yeah, that's

easy. Pre-training, that's hard." These are giant systems that people are talking

about. Inference must be easy. And so inference chips are gonna be little tiny

chips, and

... you know, they're not, they're not like NVIDIA's chips. Oh, those are gonna be

complicated and expensive, and, you know, we could make... And this is-

in the future, inference is gonna be the biggest market, and it's gonna be easy, and

we're gonna commoditize it. You know, everybody can build their own chips. And,

and that was always illogical to me because inference is thinking,

and I think thinking is hard. Thinking is way harder than reading.

You know, pre-training is just

memorization and generalization, you know, and looking for patterns in relationships.

You're reading and reading, versus thinking, reasoning, solving problems, taking

unexplored experiences, new experiences, and

breaking it down into... Decomposing it into, you

know, solvable pieces that we then go off, either through first

principle reasoning, or, you know, through previous examples,

prior experiences. You know, or just uh,

exploration and search and, you know, trying different

things. And that whole process of, of test time scaling,

Inference, is really about thinking. And it's about

reasoning, it's about planning, it's about search, it's about... And so how could that

possibly be compute light? And we were absolutely right about

that. You know, so test time scaling is intensely compute intensive.

Then the question is, okay, now we're at inference and we're at test time scaling, what's beyond that?

Well, obviously, we have now created, you know, one agentic person,

and that one agentic person has a large language model that we've now

now, you know, developed. But during test time, that agentic

system goes off and does research and bangs on

databases, and it goes out and, you know, uses tools,

and one of the most important things it does is spins off and spawns off a whole

bunch of sub-agents. Which means we're now creating large teams.

It's so much easier to scale NVIDIA by

hiring more employees than it is to scale myself.

And so the next scaling law is the agentic scaling law. It's kind of

like multiplying AI.

Multiplying AI, we could spin off agents as fast as you want to spin off agents.

And so, you know, I... You know, I have four scaling laws.

And as we use the agentic systems, they're gonna create a

lot more data, they're gonna create a lot of experiences. Some of it we're gonna say,

"Wow, this is really good. We ought to memorize this."

That data set then comes all the way back to pre-training.

We memorize and generalize it. We then refine it and fine-tune it

back into post-training. Then we enhance it even more with

test time, you know, and the agents, agentic systems,

you know, put it out to the industry. And so this loop, this

cycle, is gonna go on and on and on. It kinda comes down to basically

intelligence is gonna scale by one thing, and that's compute.

- But there's a tricky thing there that you have to anticipate and predict, which

is some of these components, it requires

different kind of hardware to really do it optimally. So you have

to anticipate where the AI innovation's going to lead. For example, a mixture of-

- Perfect

- ... experts with sparsity.

- Perfect.

- With hardware, you can't just pivot on a week's notice. You

have to anticipate what that's going to look like. It has some-

- So good

- ... that's so scary and difficult to do, right?

- For example, These AI model architectures are being invented about once every six

months. Right? And system architectures and hardware architectures

kind of every three years.

And so you need to anticipate what likely is going to happen,

you know, two, three years from now.

And there's a couple ways that you could do that. First of all, we could do research internally ourselves, and that's one

of the reasons why we have basic research, we have applied research.

We create our own models. And so we have hands-on life

experience right here. This is part of the co-design that I'm talking about.

We're also the only AI company in the world that works with literally every AI company in the world.

And so to the extent that we can, we try to get a sense

of what are the challenges that people are experiencing.

- So you're listening to the whispers across the industry, the AI labs.

- That's right. You got to listen and learn from everybody. And have a...

And then the last part is to have an architecture that's

flexible, that can adapt and move with the wind. And one of the

benefits of, of CUDA is that it's, you know, on the one hand,

an incredible accelerator. On the other hand, it's really flexible.

And so that balance, incredible balance between

specialization, otherwise we can't accelerate the CPU,

versus generalization, so that we can adapt with changing algorithms,

that's really, really important. That's the reason why CUDA has

been so resilient on the one hand, and yet we

continue to enhance it. We're at CUDA 13.2, and so we're

evolving the architecture so fast that we can stay

with, you know, with the modern algorithms.

For example... When mixtures of experts came out,

That's the reason why we had NVLink 72 instead of NVLink eight.

We could now take an entire four trillion, 10 trillion parameter model

and put it in one computing domain as if it's running on one GPU.

Um, people probably didn't notice, I said it, but

if you look at the architecture of

the Grace Blackwell racks, it was completely focused on

doing one thing, processing the LLM.

All of a sudden, one year later, you're looking at a Vera Rubin rack.

It has storage accelerators. It has this incredible new CPU called Vera.

It has Vera Rubin and NVLink 72 to run the

LLMs. It also has this new additional rack called Rock.

And so this entire rack system is completely

different than the previous one, and it's got all these new

components in it. And the reason for that is because the last one was designed to run

MoE large language models, inference. And this one is to run agents and agents

bang on tools, and-

- Obviously, the design of the system had to have been done before Claude Code,

Codex, OpenClaw. So you were anticipating the future, essentially.

And that comes from what? From the whispers, from understanding what all the state-

- No

- ... of the art is about?

- No, it's easier than that. You just reason about it. First of all, you just reason.

no matter, no matter what happens, at some point in order

for that large language model to be a digital worker... Let's just,

let's just use that metaphor. Let's say that we want the LLM to be a

digital worker. What does that have to do?

It has to access ground truth. That's our file system. It has

to be able to do research. It doesn't know everything. We don't have... And I don't wanna

wait until this AI becomes, you know, universally

smart about everything, past, present, and future before I

make it useful. And so therefore, I might as well let it go do research.

It's obviously, if it wants to help me, it's gotta use my tools.

You know, a lot of people would say, "You know AI is gonna completely

destroy software. We don't need software anymore. We don't even need tools anymore." That's

ridiculous. Let's use the... Let's use a thought experiment.

And you could just sit there, enjoy a glass of whiskey, and,

and think about all these things, and it would become completely obvious. Like, if I

were to create the most amazing- the

most amazing agent that we can imagine in the next 10 years.

Let's say it'd be a humanoid robot. If that humanoid robot were to be created,

is it more likely that the humanoid robot comes into my house

and uses the tools that I have to do the work that it needs to do?

Or does this hand turns into a 10-pound hammer in

one instance, turns into a scalpel in another instance,

and in order to boil water, it beams, you know, microwaves out

of its fingers? You know, or is it more likely just to use a microwave, you

know? And the first time it goes up to the microwave,

it probably doesn't know how to use it. But that's okay. It's connected to the

internet. It reads the manual of this

microwave, reads it, instantly becomes an expert.

And so it uses it. And so I think the... I just

described, in fact, almost all of the properties of OpenClaw.

You know, that it's gonna use tools, that it's gonna access files, it's gonna be able to do

research. It has I/O subsystem. And when you're done

reasoning through it, reasoning about it in that way,

Then you say, "Oh, my gosh, the impact to the future of computing is deeply

profound." And the reason for that is, I think we've just reinvented the computer.

And then now you say, "Okay, when did we reason about that? When did we

reason about OpenClaw?" If you take the OpenClaw schematic that I used at GTC,

you'll find it two years ago. Literally, two years ago at GTC, I was talking about

agentic systems that exactly reflect OpenClaw today.

And, of course, the confluence of

many things had to happen. First of all, we needed Claude and

GPT and, you know, all of these models to reach a level of

capability. So their innovation and their breakthroughs and their continued

advances was really important.

And then, of course, somebody had to create an open source, you

know project that was sufficiently robust, you know, and sufficiently complete

and that we can all put to work. And I think OpenClaw

did for, did for agentic systems what ChatGPT did for

generative systems. And I just think it's a very big deal.

- Yeah, it's a really special moment. I'm not exactly sure why it

captured so much of the world's attention, but it did,

more than Claude Code and Codex and so on.

- Because consumers could reach it.

- Sure, yeah. But there's also so much of this is vibes.

And Peter, I had a podcast with him, he's a

wonderful human being. So part of it is also the humans that represent the thing.

- Yeah, no doubt.

- Part of it is memes and the— 'Cause we're all trying to figure it out.

There's really serious and complicated security concerns

about when you have such powerful technology, how do you hand over your data

so they can do useful stuff? But then there's scary things associated with that.

And we, as a civilization, as individual people and as a civilization, figuring out how

to find that right balance.

- Yeah, we jumped on it right away and we sent a bunch of security experts this way.

And we did this thing called OpenShell. It's already been

integrated into, into OpenClaw.

- And NVIDIA put forward NemoClaw.

- Yep, exactly.

- They install super easy. It makes sure that it's secure.

- We give you two out of three rights. Agentic systems can access

sensitive information, it can execute code, and it can communicate

externally. We could keep things safe if we gave you two out of those three

capabilities at any time, but not all three.

And out of those two out of three capabilities, we also give you access control

based on whatever rights that you're given by enterprise.

And then we connect it to a policy engine that all these enterprises already have.

And so we're going to try to do our best to help OpenClaw become a better claw.

- So you eloquently explained how we have a long history of

blockers that we thought were going to be blockers, and we overcame them. But now looking into the

future, what do you think might be the blockers now that it's clear that agents

will be everywhere?

So obviously we're going to need compute. So what is going to be the

blocker for that scaling?

- Power is a concern, but it's not the only concern. But that's the reason why

we're pushing so hard on extreme co-design, so that we can improve the tokens

per second per watt orders of magnitude

every single year. And so in the last 10 years, Moore's

Law would have progressed computing about 100 times in the last

10 years. We progressed and scaled up computing by

a million times in the last 10 years. And so we're gonna keep on doing that

through extreme co-design. So energy efficiency, perf per watt,

completely affects the revenues of a company. It

affects the revenues of a factory. And we're just going to

push that to the limit so that we can keep on driving token costs

down as fast as we can. You know, the

our computer price is going up, but our token generation

effectiveness is going up so much faster that token cost is

coming down. It's just coming down an order of magnitude every year.

- So power, that's an interesting one. So the way to try

to get around the power blocker is to try to, with the tokens per

second per watt, try to make it more and more efficient.

Of course, there's the question of how do we get more power.

- We should also get more power.

- That's a really complicated one. You've talked about small modular nuclear power plants.

There's all kinds of ideas for energy. How much does it keep you

up at night? The bottlenecks in the supply chain of AI,

like ASML with EUV lithography machines, TSMC with advanced

packaging like CoWoS, and SK Hynix with the high bandwidth memory?

- All the time, and we're working on it all the time. No company in history

has ever grown at a scale that we're growing while

accelerating that growth. It's incredible.

And it's hard for people to even understand this.

In the overall world of AI computing, we're increasing

share. And so supply chain, upstream and downstream, are really important to us.

I spend a lot of time informing all the

CEOs that I work with, what are the dynamics that's going to cause,

The growth to continue or even accelerate? It's part of the reasons

why to the entire right-hand side of me were CEOs of

practically the entire IT industry upstream and practically the entire

infrastructure industry downstream.

And they were all... There were several hundred CEOs.

And I don't think there's ever been keynotes where several hundred CEOs show up.

And part of it is, I'm telling them about our business condition now.

I'm telling them about the growth drivers in the very near future and what's

happening. And I'm also describing where are we going to go next

so that they could use all of this information and all of the dynamics that are

here to inform how they want to invest. And

so I inform them that way like I inform my own employees.

And then of course, then I make trips out to them

and make sure that, "Hey, listen, I want you to know this quarter, this

coming year, this next year, these things are going to happen."

And if you look at the CEOs of the DRAM industry,

The number one DRAM in the world was DDR memory for CPUs in data centers.

About three years ago, I was able to convince several of the CEOs that

even though at the time HBM memory was used quite scarcely, you know, and,

and barely by supercomputers that this was going to be a

mainstream memory for data centers in the future. And at first it sounded

ridiculous, but several of the CEOs believed me and decided

to invest in building HBM memories. Another memory was rather odd to put into a data

center, is the low power memories that we use for cell phones.

And we wanted them to adapt them for supercomputers in the data

center. And they go, "Cell phone memory for supercomputers?"

And I explained to them why. Well, look at these two memories,

LPDDR5, HBM4. The volumes are

so incredible. All three of them had record years in history, and these

are, these are 45-year companies. And so,

you know, I... That's part of my job, is to inform and shape, inspire, you know.

- So you're not just manifesting the future and maybe

inspiring NVIDIA, the different engineers of the company, you're,

you're manifesting the supply chain of the future. So you're having conversations with

TSMC, with ASML.

- Upstream, downstream.

- Upstream, downstream. So that's the thing.

- GEV, Caterpillar. Yeah, that's downstream from us. Yeah, yeah, there you go.

- Yeah, the whole thing. I mean, but that's so...

There's so much incredibly difficult engineering that

happens in the entire semiconductor industry, and it just feels scary

how intricate the supply chain is, how many components there

are, but it works somehow.

- Exactly, the deep science. The deep engineering, the incredible

manufacturing, and so much of the manufacturing is already robotics,

but we have a couple of hundred suppliers that contribute

the technology that goes into our 1.3 million component rack.

Each rack is 1.3, one and a half million components. There are 200 suppliers

across the Vera Rubin rack.

- So it's interesting that you don't list that as the thing that keeps you up at night in the list of

blockers.

- But I'm doing, I'm doing all the things necessary to-

- Okay

- ... yeah, see?

I can go to sleep because I checked it off. I said, okay, you know, I go, I

yeah, I can go to sleep and I go, well, let's see, what re- let's reason

about this. What's important for us?

Um, because okay, let's reason about this. Because we

changed the system architecture from the original DGX-I that

you remembered to NVLink-72 rack scale computing-

... what's gonna... What does that, what does that mean?

What does that mean to software? What does that mean to engineering? What does that

mean to how we design and test? How, and what does that mean to the supply

chain? Well, one of the things that it meant was we moved supercomputer,

supercomputer integration at the data center

into supercomputer manufacturing in the supply chain. If you're doing that,

you also have to recognize you're gonna move one... And if,

if you're, if you're, you know, total footprint of

whatever data center you're gonna build, let's say you would like to have, you know,

50 gigawatts of supercomputers that are running simultaneously,

and it takes one week to manufacture that 50

gigawatts of supercomputers, then each week in the

supply chain, the supercomputers are gonna need a gigawatt of power. And so,

so we're gonna need the supply chain to increase the amount of power it has

to build, test, to build and test the

supercomputers in the supply chain before I ship it.

Well, NVLink-72 literally builds supercomputers in the supply chain and

ships 'em two, three tons at a time per rack.

It used to be—they used to come in parts and we used to assemble 'em

inside the data center. But that's impossible now because NVLink-72 is so dense.

And so that's an example. And I would have to go and to, you know,

I've... Fly into the supply chain, go meet my partners saying, "Hey," I said, "guess

what? So here's what I'm going to do with... This is the way we used to build

our DGXs. We're gonna build them this way. This is gonna be so much better because

we're going to need 'em for inference." The market for inference is, you know,

coming. The inflection point for inference is coming. It's gonna be a big market.

And so I first explain to them what's going on, why it's gonna happen, and

then I ask 'em to make several billion dollars of capital investments

each. And because they, you know, they trust me and I'm very

respectful of 'em, and I give 'em every opportunity to question me and

I spend time to explain things to people and I reason about it. I draw on pictures

and I reason about it in first principles. And by the time

I'm done with them, they know what to do.

- So it's a lot of it is about relationships and building a shared view of the future.

Uh, but do you worry about certain bottlenecks? I mean, what are the

biggest bottlenecks in the supply chain? Are you, are you worried about ASML's EUV

tooling? Are you, are you worried about the packaging, CoWoS packaging of

TSMC, about how fast it could scale? Like you said, you're

not only growing incredibly fast, you're accelerating your growth. So

it feels like everybody in the supply chain, and those are

certainly bottlenecks, would have to scale up. Are you having conversations with them,

like, how can you scale up faster? Do you worry about it?

- No.

- Okay.

- Because, because I told 'em what I needed. They understood what I

need. They told me what they're gonna go do, and I believe them what they're going to do.

- Interesting.

That's great to hear. So maybe if we can just linger on the power for a little

bit. What are your hopes for how to solve the energy problem?

- One of the areas, Lex, that I'm

that I would love, I would love us to talk about and just get the message out,

you know our power grid is designed

for the worst case condition with some margin. Well,

99% of the time we're nowhere near the worst case condition because the worst case condition

is a few days in the winter, a few days in the summer,

and extreme weather. Most of the time we're nowhere

near the worst case condition and we're probably running around, call it

60% of peak. And so 99% of the time, our power grid

has excess power, and they're just sitting idle, but they have to be there

sitting idle because just in case, when the time comes, hospitals have to

be powered and, you know, infrastructure has to be powered and airports have to run and so on and so

forth. And so the question that I have is whether we could go and,

Help them understand and create contractual agreements

and design computer architecture systems, data centers, such that when they need,

The maximum power for infrastructure in society,

that the data centers would get less.

But that's in a very rare instance anyways. And during that time, we either have a

backup generator for that little part of it, or we just have our computers shift the

workload somewhere else, or we have the computers just run

slower. You know, we could degrade our performance,

reduce our power consumption and provide

for a, you know, slightly longer latency response, you know, when

somebody asks for, you know, asks for an answer. And so I think that

that way of using computers, of building data centers,

instead of expecting 100% uptime-...

and these contracts that are really, really quite rigorous, it's

putting a lot of pressure on the grid to be able to... Now, they're gonna

have to increase from their maximum. I just wanna use their excess.

It's just sitting there.

- Yeah, that's not talked about enough. So what's stopping there?

Is it regulation? Is it bureaucracy?

- I think it's a three-way problem. It starts with the end

customer. The end customer puts requirements

on the data centers that they can never

not be available, okay? So that the end customer expects perfection.

Now, in order to deliver that perfection, you need a combination of

backup generators and your grid power

supplier to deliver on perfection. And so everybody's gotta have six nines.

Well, I think first of all,

right now, we ought to have everybody understand that when the customer asks for these

things, you have somebody in your data center operations

team disconnected from the CEO. I bet the CEO doesn't know this. I'm

gonna talk to all the CEOs. The CEOs are probably

not paying any attention to the contracts that are being signed, and

so everybody wants to sign the best contract, of course. And they go down to

cloud service providers, and the contract, the... The two contract

negotiators that are... You, I could just see them now.

You know, negotiating these multi-year contracts. Both sides want,

you know, the best contract. As a result,

the CSPs then have to go down to the utilities, and they expect the nine,

the six nines. And so I think the first thing is just make

sure that all of the customers, the CEOs and the customers

realize what they're asking for.

Now, the second thing is we have to build data centers that gracefully degrade.

And so if the power, if the utility, if the grid tells us, "Listen, we're gonna have

to back you down to about 80%," we're gonna say, "That's no problem at all."

We're just gonna move our workload around. We're gonna make sure that data's never

lost, but we can reduce the computing rate and use less

energy. The quality of service degrades a little bit. For the critical

workloads, I shift that somewhere else right away

so I don't have that problem, and so, you know, whoever, whichever data

center still has 100% uptime, and so...

- How difficult of an engineering problem is that, that smart, dynamic allocation of power in a data

center?

- As soon as you could specify, you could engineer it. U- beautifully put.

So long as it obeys the laws of physics on first principles, I think we're good.

- What was the third thing you were mentioning? Um...

- So the second thing is the data centers. And the third thing is we

need the utilities to also recognize that this is an opportunity-

... and instead of saying, "Look it's gonna take me five years

to increase my grid capability," uh, if

you, if you have, if you're willing to take power of this level of guarantee,

I can make them available for you next month and at this price.

And so if utilities also offered more segments of power delivery promises,

then I think everybody will figure out what to do with it.

Yeah, but there's just way too much waste in the grid right now. We should go after it.

- Uh, you've highly lauded Elon and xAI's

accomplishment in Memphis, in building Colossus supercomputer,

probably in record time in just four months. It's now at 200,000

GPUs and growing very quickly. Is there something that you could speak

to the understand about his approach that's instructive to, broadly

to all the data center creators that's

that enabled that kind of accomplishment? His approach to engineering, his approach to the

whole management of construction, everything?

- First of all, Elon is deep in so many different topics. Um,

Yet he's also a really good systems thinker.

And so he's able to think through multiple disciplines, and,

and he obviously pushes things,

questions everything, where they're, number one, is it necessary?

Number two, does it have to be done this way? And then numbers, you know, does it have,

does it have to take this long? And, and so, so he, he has, he has the a- he

has the ability to question everything,

To the point where everything is down to its minimal amount

that's necessary, you can't take anything else out. And yet the,

the necessary capabilities of the product remains, you

know? And so he's, he is as minimalist as you could possibly imagine,

and he does it at a system scale. I think... I also love the fact that he is

he is represented. He is present at the point of action.

You know, he'll just go there. If there's a problem, he'll just go there

and then, "Show me the problem." You know, when you do all of this in combination,

you overcome a lot of previous, "This is just the way we do it."

"Um, you know, I'm waiting for them.

Uh," You know, I mean, it's just, everybody has a lot of excuses. And

so, and then the last thing is when you act personally with

so much urgency it causes everybody else to act with urgency,

you know? And every supplier has a lot of customers going on. Every supplier

has a lot of projects going on,

and he made it his... He makes it his business that he's

the top priority of everybody else's, you know, projects. And so he does that by

demonstrating it.

- Yeah, I've been in a bunch of those meetings. It's just, it's fun to watch, 'cause really, not enough

people ask the question like, "Okay, so, can this be done a lot faster, and how?

Why does it have to take this long?"

- Yeah, right.

- And then in the... That becomes an engineering question often. And yes, I think when

you get the ground truth of actually... I remember,

one of the times I was hanging out with him, he literally is going through the

entire process of how to plug in cables into a rack. He's,

he's working with an engineer on the ground that's doing that task, and he's

just trying to understand what does that process look like so it can be less

error-prone. And just building up that intuition from every single task involved in,

putting together a data center— ...you start to immediately get a sense at the

detailed scale and at the broad systems scale of

where the inefficiencies are, and so you can make it more and more and more efficient.

Plus you have the big hammer of being able to say, "Let's do it totally different-"

- Yeah. That's right.

- "... and remove all possible blockers."

- That's right.

- Is there parallels in the NVIDIA Extreme Systems co-design approach that you

see in the way Elon approaches systems engineering?

- Well, first of all, co-design is an ultimate systems engineering problem.

And so we approach, we approach the work that we do from that first, from that

The other thing that we do and this is, this is a philosophy, a thought,

a state of mind, I guess, a method,

That I started 30 years ago, and it's called the

speed of light. The speed of light is not just about the speed. The speed of light is, is

my shorthand for what's what's the

limit of what physics can do. And so every single, everything,

everything that we do is compared against the speed of light. Memory speed,

Math speed, power, cost, time, effort, number of people,

manufacturing cycle time. And when you think about latency versus throughput,

When you think about cost versus throughput, cost

versus capacity, all of these things, You test against the speed of light to

achieve all of these different constraints separately. And then when you consider it

together, you know you have to make compromises because a

system that achieves extremely low latency versus a cheap, a system that

achieves very high throughput are architected fundamentally differently.

But you want to know what's the speed of light of a system that

achieves high throughput, what's the speed of light of a system that

achieves low latency?

And then when you think about the total system, you can make trade-offs. And so

I force everybody to think about what's the, what the first- the first principles,

the limits- ... the physical limits, For everything before we, you know,

before we do anything. And we test everything

against that. And so that's a good frame of mind.

I don't love the other methods, which is continuous improvement.

The problem with continuous improvement, it...

First of all, you should engineer something from first principles at the speed,

you know, with speed of light thinking. Limit it only by physical limits,

and physics limits. And after that, of course you would improve it over time. Um,

but I don't like going into a problem and somebody says, "Hey, you know, it takes 74 days

to do this today-" "... Right now. And we can do it for you in 72 days."

You know, I'd rather strip it all back to zero-

... and say, "First of all, explain to me why 74 days in the first place.

And let's note, let's think about what's possible today. And if I were

to build it completely from scratch, you know, how long would it take?"

Oftentimes, you'd be surprised. It might come to six days.

Now, the rest of the six days, the 74, could be

very well-reasoned and compromises, and, you know, cost

reductions, and all kinds of different things. But at least you know what they are.

And then now that you know that six days is possible,

then the conversation from 74 to six, surprisingly much more effective.

- In such incredibly complex systems that you're working with, is simplicity sometimes

a good heuristic to reach for? I mean, if I can just...

I mean, the pod, the Vera Rubin pod that you announced is just incredible.

We're talking about seven chips, seven chip types, five purpose-built

rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 NVIDIA dies,

over 1,100 Rubin GPUs, 60 exaflops, 10 petabytes per second of scale bandwidth.

That's all just one...

- That's just one pod.

- That's just one pod.

- Yeah, that's just one pod.

- I mean, in- ... so you have the... And then even the NVL 72 rack

alone is 1.3 million components,

1300 chips, 4,000 pods crammed into a single 19-inch wide rack.

- And Lex, we're probably gonna have to crank out about 200 of these pods a week,

just to put it in perspective.

- The amount of different components, I suppose simplicity is impossible,

but is that a metric that you kind of reach for in trying to design things?

- You know, the phrase, the phrase that I use most often is, we-

we need things to be as complex as necessary, but as simple as possible.

And so the question is, is all that complexity there

necessary? And we ought to test for that.

And we got to challenge that. And then after that, everything else above it,

you know, is gratuitous.

- But it's still almost incredible. Semiconductor industry broadly, but what NVIDIA is

doing,

some of the greatest engineering in history. So these systems are just truly,

truly marvels of engineering.

- It is the most complex computer the world has ever made.

- Yeah, the engineering teams, I mean-

... I don't, it's not a competition, but I don't know. If it was like an Olympics of

engineering teams, I mean, TSMC does incredible engineering. Like I said,

ASML at every scale, but NVIDIA is gonna give them a run for their money.

Just incredible, incredible teams.

- Well, it's gold medalists in every single, in every single sport, all

assembled right here.

- And have to work together.

And report directly to you. This is wonderful. You recently traveled to China.

so it's interesting to ask you China's been incredibly

successful in building up its technology sector. What do you

understand about how China's able to, over the

past 10 years, build so many incredible world-class companies,

world-class engineering teams, and just this technology ecosystem-

... that produces so many incredible products?

- A whole bunch of reasons for, well, first of all, let's start with some facts.

50% of the world's AI researchers are Chinese, plus or minus, and

they're mostly in China

still. We have many of them here, but there's amazing researchers still in China.

They, their tech industry showed up at precisely the right time.

At the time of the mobile cloud era their way of

contributing was software, and so this is a country's incredible science

and math. Really well-educated kids.

Their tech industry was created during the era of software.

They're very comfortable with modern software.

China is not one giant economic country.

It's got many provinces and cities with mayors all

competing with each other. That's the reason why there's so many EV

companies. That's the reason why there's so many AI companies. That's the reason why there's so

many, every company you could imagine, they all create some of them. And as

a result, they have insane competition internally.

And, you know, what remains is an incredible company.

They also have a social culture where it's family first, friends second, and

company third. And so, the amount of conversation that goes back and

forth between... They're essentially open source all the time.

So the fact that they contribute more to open source is so sensible

because they're probably, "What are we protecting?" You know, my engineers,

their brothers are in that company, their friends are in that company, and they're all

schoolmates. You know, the schoolmate concept. There's a, you know, one

schoolmate, you're brother for life. And so they,

they share knowledge very, very quickly.

And so there's no sense keeping technology hidden. You might as well put it on

open source. And so the open source community then amplifies,

accelerates the innovation process. So you get this rapid, incredible great talent,

rapid innovation because of open source and just, you know, the nature of friends,

and insane competition. Among comp-

among the company, what emerges is incredible stuff. And so

this is the fastest innovating

country in the world today, and this is something that has everything that, everything that I've just

said is fundamental to just how the kids were grown,

the fact that they have excellent education, the fact that they, parents

want them to do well in school, the fact that they, their culture is that

way. These are, you know, these are just the thing about their country,

and they showed up at precisely the time when technology is going through that

exponential.

- Plus culturally, it's pretty cool to be an engineer.

It connects to all the components that you're mentioning...

- It's a builder nation.

- It's a builder nation.

- Yeah, it's a builder nation. Our country's leaders,

incredible, but they're mostly lawyers. Their country's

leaders, and because we're, they're trying to keep us safe, rule of law, governing,

their country was built out of poverty. And so

most of their leaders are incredible engineers. Some of the brightest minds.

- To take a small tangent, because you mentioned open source, I have to

go to Perplexity here, who you have been a fan of a long time.

- Love it, yeah.

- And thank you for releasing open source Nemotron 3 Super,

which you can also use inside Perplexity to look stuff up.

Now, which is 120 billion parameter open weight MoE model.

What's your vision with open source? So you mentioned China with

DeepSeek and MiniMax, with all these companies really

pushing forward the open source AI movement, and NVIDIA is really leading the

way in close to state-of-the-art open source LLMs. What's your vision there?

- First, if we're gonna be a great AI computing company, we have to understand how AI

models are evolving. One of the things that I love about Nemotron 3 is it's not a,

just a pure transformer model, it's transformer and SSMs. And we were early in,

Developing the conditional GANs, which,

that progressive GANs, which led step-by-step to diffusion. And so,

The fact that we're doing basic research

in model architecture and in different domains

gives us visibility into, you know, what kind of computing

systems would do a good job for future models. And so it is part

of our extreme co-design strategy. Second, um, I think we rightfully recognize

that on the one hand, we want world-class

models as products, and they should be proprietary.

On the other hand, we also want AI to diffuse into every industry and every country,

every researcher, every student.

And if everything is proprietary, it's hard to do research

and it's hard to innovate on top of, around, with.

And so...Open source is fundamentally necessary for many industries to join the AI

revolution. NVIDIA has the scale and we have the motives

to not only skills, scale, and motivation to build and continue to

build these AI models for as long as we shall live.

And so therefore, we ought to do that. We can open up, we can

activate every industry, every researcher,

you know, every country to be able to join the AI revolution.

There's the third reason, which is from that, to

recognizing that AI is not just language. These AIs will likely use tools and

models and sub-agents that were

trained on other modalities of information. Maybe it's

biology or chemistry or you know,

laws of physics, or you know, fluids and thermodynamics, and not

all of it is in language structure.

And so somebody has to go make sure that weather prediction,

biology, AI, AI for biology, physical AI, all of that stuff stays, can be pushed

to the limits and pushed to the frontier. We don't build cars, but we wanna make sure

every car company has access to great models. We don't,

discover drugs, but I wanna make sure that Lilly has the world's best

biology AI systems, so that they can go use it for discovering drugs.

And so these three fundamental reasons, both in,

recognizing that AI is not just language, that AI is really broad,

that we wanna engage everybody into the world of AI, and then also co-design of AI.

- Well, I have to say, once again, thank you for open sourcing,

really truly open sourcing Nemotron 3 and ...

- Yeah, I appreciate you were saying that. We open sourced the models, we open sourced the weights, we open

sourced the data, we open sourced how we created it. Yeah, it's pretty amazing.

- Uh, it's really, it's really incredible.

You're originally from Taiwan and have a close relationship with TSMC.

So I have to ask, TSMC I think

also is a legendary company in terms of the engineering teams, in terms of the

incredible engineering work that they do. What

what do you understand about TSMC culture and their approach that

explains how they're able to achieve this singular

unmatched success in everything they're doing with semiconductors?

- You know, first of all, the deepest misunderstanding about TSMC is that

their technology is all they have. That somehow they have a really great

transistor, and if somebody shows up another transistor, game over.

It's the technology

and of course, you know, I don't mean just the transistor, the metallization

systems, the packaging, the 3D packaging, the silicon photonics,

the, you know, all of the technology that they have. That technology is really what

makes the company special. Their technology makes the company special.

But their ability to orchestrate

the demands, the dynamic demands of hundreds of companies in the world as

they're moving up, shifting out,

you know, increasing, decreasing, push, pushing out, pulling

in changing from customer to customer, Wafer starting, wafer stopping,

Emergency wafer starts, you know, all of this

dynamics of the world's complexity as the world is

shape-shifting all the time, and somehow they're

running a factory with high throughput, high yields,

really great costs, excellent customer service.

They take their work, they take their promises seriously. They, when your

wafer, because they know that they're helping you run your company, when the wafers,

when the wafers were promised to show up, the wafers show up, you know, so that you could

run your company appropriately. And so their system, their

manufacturing system is completely miraculous, I would say. Then

the second thing is their culture. This culture is simultaneously,

Technology focused on one hand, advancing technology,

simultaneously customer service oriented on the other hand. A lot of

companies are very customer service oriented, but they're not very

technology excellent. They're not at the bleeding edge of technology.

There are a lot of companies who are tech, at the bleeding edge of technology, but they're not the best customer

service oriented company. And so it just depends on somehow they've,

they've balanced these two and they're world-class at both.

And then probably the third thing is the technology that I most

value in them that they created this, you know, this,

Intangible called trust. I trust them to put my company on top of them. That's a

very big deal.

- When they trust, I mean, there's a really close relationship there that you've established, and that trust

is established based on many years of performance, but there's human

relationships involved there as well.

- Three decades, I don't know how many tens, hundreds of billions of dollars

of business we've done through them, and we don't have a contract.

That's pretty great.

- Amazing. Okay, there's this story

... ... That in 2013, the founders of TSMC, Morris Chang

offered you the chance to become TSMC's chief executive,

And you said you already had a job. Is this story true?

- Story is true. I didn't dismiss it. Um but I was deeply honored and, and of course,

of course uh, I knew then as I know now, TSMC is one of the

most consequential companies in history.

And, and Morris is one of the highest regarded executive and,

and business and personal friend that I've had in my life.

And, um ...Uh, for him to ask is, uh um, I was humbled and, and really honored.

But, but the work that I'm doing here is really important, and I've seen,

you know, in my mind's eye, what NVIDIA

was going to be and what the impact that we could have.

And uh, it was really important work.

And it's my responsibility, you know, my sole responsibility to make this

happen. And so I uh, I declined it, You know,

not, not because it wasn't an incredible offer.

It's an unbelievable offer but, but I simply couldn't take it.

- I think NVIDIA, both NVIDIA and TSMC are two

of the greatest companies in the history of human civilization. And running either

one, I'm sure, is incredibly complicated effort and takes...

You have to truly be all in.

Uh, everybody at every scale, not just at the CEO level. Everybody is really

truly all in-

- Yeah. Yeah, no doubt

- ... To, to accomplish this kind of complexity.

- So now I can help both companies.

- Exactly. So NVIDIA is now the most valuable company in the world.

I have to ask, what is the NVIDIA's biggest moat,

as the folks in the tech sector say?

The edge you have that protects you from the competition.

- Our single most important property as a company

is the install base of our computing platform.

Our single most important thing today is our, is the install

base of CUDA. Now, the reason why,

uh, 20 years ago, of course, there was no install base.

But what makes... And if somebody came up with a

GUDA or TUDA it wouldn't make any difference at

all. And the reason for that is because it's

never been just about the technology. The technology, of course, was

incredible visionary. But it's the fact that the company was dedicated to it,

stuck with it, expanded its reach. Um,

it wasn't three people that made CUDA successful. It was

43,000 people that made CUDA successful. And the several

million developers that believed in us,

That trusted that we were going to continue to make CUDA 1, 2, 3, 13,

that they decided to port and dedicate their software on top of it, their

mountain of software on top of it. And so the install base

is the number one most important advantage.

That install base, when you amplify it with the velocity of our

execution at the scale that we're talking about, no

company in history had ever built systems of this

complexity, period. And then to build it once a year is impossible.

And that velocity combined with the install base,

in the developer's mind, you just go now, take the developer's mind.

From the developer's perspective, if I support CUDA,

tomorrow it'll be 10 times better. I just have to wait six months on average.

Not only that, if I develop it on CUDA, I reach a few hundred million people,

computers. I'm in every cloud, I'm in every computer company, I'm in every single

industry, I'm in every single country.

So if I create an open source package and I put it on CUDA first,

I get these both attributes simultaneously. And not only that,

I trust 100% that NVIDIA is going to keep CUDA around and maintain it and

improve it and keep optimizing the libraries for as long as they shall live.

You could take that to the bank, and that last part, trust.

You put all that stuff together, if I were a developer today,

I would target CUDA first. I would target CUDA most.

And that's the reason that I think in the final analysis is

our first, that's even our first-

- core advantage. Our second one is our ecosystem.

The fact that we vertically integrated this incredibly complex system,

but we integrate it horizontally into every single company's computers.

- We're into Google Cloud, we're into Amazon, we're in Azure. You know, we're ramping up

AWS like crazy right now. We're in new companies like

CoreWeave and Nscale. We're in supercomputers at Lilly.

We're in enterprise computers. We're at the edge in radio base

stations. You know, I mean, it's just crazy. One architecture is in

all these different systems. We're in cars, we're in robots, we're in satellites, we're out in

space. And so the fact that you have this one architecture and the

ecosystem is so broad, it basically covers every single industry in the world.

- Well, how does the, how does the CUDA install

base evolve into the future with AI factories as a

moat? What do you... Do you think it's possible that NVIDIA of the future is all about

the AI factory?

- Well, the unit of computing used to be GPU to us.

Then it became a computer, then it became a cluster. Now

it's an entire AI factory. When I see a computer, when I see what NVIDIA

builds, in the old days, I would, you know, I visualize the chip.

And then when I announced the new product, new generation, like,

"Ladies and gentlemen, we're announcing Ampere today," I'd pick up the chip.

That was my mental model-

... of what I was building. Today, I wouldn't... Picking up

the chip is kind of still adorable. But it's adorable. It's not my mental

model of what I'm doing. My mental model is this giant gigawatt

thing that has power generations connected to the grid. It's got cooling

systems and networking of incredible monstrosity, you

know. 10,000 people are in there trying to install it,

hundreds of networking engineers in there, thousands of engineers behind it trying

to power it up.

You know, powering up one of those factories, as you know, it's not somebody going,

"It's on now." It takes thousands of people to bring it up.

- So mentally, you're actually... When you're thinking about a single unit of compute, you're like

literally, when you go to bed at night, you're thinking now about collection of racks,

so pods, not individual chips.

- Entire infrastructure. And I'm hoping my next click is when I'm thinking about building

computers, it's, you know, planetary scale. That'll be the next click.

- Well, what do you think about the space angle that Elon has

talked about, doing compute in space for solving some

of the... It makes some of the energy issues in terms of scaling energy easier.

- Cooling issues is not easy. Yeah.

- Cooling. Well, there's a large number of engineering complexities involved with

that. So, so what... You know, NVIDIA has also announced that you're

already thinking about that.

- Yeah, we're already there. NVIDIA GPUs are the first GPUs in space.

And I didn't realize it, it was so interesting

to... I would have declared it maybe.

We're in space. You know, little, little astronaut suit on one of our GPUs.

But we've been in space. It's the right place to do a lot of imaging.

You know, because those satellites have, have really high resolution imaging systems,

and they're sweeping the Earth, you know, continuously now. And,

um, you want, you know, centimeter scale, imaging

imaging that is done continuously for the world, so that,

you know, you'll basically have real time telemetry of

everything. You don't wanna beam that back down to earth. It's just, you know,

petabytes and petabytes of data. You gotta just do AI right there at the edge,

throw away everything you don't need, you've seen before, didn't change, and then just

keep the stuff that, that you need. And so AI had to be done at the edge.

Obviously we have, we have a 24/7 solar, if we put it at the polars. And uh,

but, you know, there's no conduction, no convection.

And so, you know, you're pretty much just radiation.

And uh, but, you know, space is big. I guess, you know, we're just gonna put

big, giant radiators out there.

- How crazy of an idea do you think it is? Like is this, is this five years out, 10

years out, 20 years out? So we're talking about blockers for AI scaling.

- You know, I'm just so much more practical. I look for where, where, um

my next, next bucket of opportunities are first.

Meanwhile, I'm cultivating space. And so I send

engineers to go work on the problem. We're starting to... We're

learning a lot about it. How do we deal with radiation? How do we deal with

degrading performance? How do we deal with a continuous,

Testing and attestation of, of de- defects?

And you know, how do we deal with redundancy? And how do we

degrade gracefully and things like that? And so we could, we could do a...

What about software? How do you think about software and redundancy and

performance out in space? Make it so that the computer

never breaks, it just gets slower, you know.

And I... So we could start doing a lot of engineer

exploration upfront. But in the meantime, my, my favorite answer

is ge- eliminate waste.

You know, we've got all that idle power, I want to evacuate it as fast as possible.

- Yeah. There's a lot of low-hanging fruit here on Earth-

... That we can utilize for the AI scaling. Quick pause.

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Choose wisely, my friends. And now, back to my conversation with Jensen Huang.

Do you think NVIDIA may be worth 10 trillion at some point?

Let's ask it this way. What does the future

of the world look like where that's true?

- I think that NVIDIA's growth is Extremely likely, and in my mind, inevitable. And

let me explain why. We're the largest computer company in history.

That alone should beg the question, why?

And the reason of course... Two reasons. First, two foundational technical reasons.

The first reason is that computing went from being a retrieval-based, file

retrieval system. Almost everything is a file... We pre-write something, we

pre-record something. You know, we draw something, we put it on the web, we put it

in a file. And we use a recommender system, some smart

filter, to figure out what to retrieve for you. And so we were a

pre-recording, human pre-recording, and file retrieving

system. That's what a computer is, largely.

To now, AI computers are contextually aware,

which means that it has to process and generate tokens in real time.

So we went from a retrieval-based computing system to a generative-based

computing system.

We're gonna need a lot more processing in this new world than in the old

world. We need a lot of storage in the old world. We need a lot

of computation in this new world. And so,

so that's the first part of it. We fundamentally changed computing and the way

how computing is done. The only thing that would cause it to go

back...... is if this way of computation,

this way of computing generating information that's contextually relevant,

situationally aware, that is grounded on new insight before it

generates information, this computation-intensive way of doing computing

would only go back if it's not effective.

So if... For the last 10, 15 years while working on deep learning,

if at any single moment

I would have come to the conclusion that, "You know what? This is

not gonna work out.

I think this is a dead end." Or, "It's not gonna scale, it's not gonna solve this modality, not gonna be

used in this application." Then, of course, I would feel very differently about it,

but I think the last five years has given me more confidence

than the last ten year, previous ten years. The second idea,

is computers, because it was a storage system, it was largely a warehouse.

We're now building factories. Warehouses don't make much money.

Factories directly correlates with the company's revenues. And so,

the computer did two things. Not only did it change the way it did it,

its purpose in the world changed. It's no longer a computer, it's a factory.

It's a factory, it's used for generation of revenues.

We're now seeing not only is this factory generating

products, commodities that people want to consume,

we're seeing that the commodities are so

interesting, so valuable, so, to so many different audiences

that the tokens are starting to segment, like iPhones. Mm-hmm.

You have free tokens,

you have premium tokens, and you have several tokens in the middle.

Yeah. And so intelligence, as it turns out,

you know, it's a scalable product. There's extremely high intelligence

products, tokens that you could... that are used for specialized things, people be

willing to pay. You know, the idea that somebody's willing to

pay $1000 per million tokens is just around the corner. It's not if, it's only when.

And so now we're seeing that the

commodity that this factory makes is actually valuable, and is revenue

generating and profit generating. How, now the question is how

many of these factories can, does the world need?

How much, how many tokens does the world need? And

how much is society willing to pay for these tokens? And

what would happen to the world's economy

if the productivity were to improve so substantially?

What would happen... Are we, are we gonna discover new drugs, new products,

new services? And so when you take these things in combination, I am absolutely

certain that the world's GDP is going to accelerate in growth.

I'm absolutely certain the percentage of that GDP that will be used for computation

will be 100 times more than the past... because it's no

longer a storage unit. It's a product generation unit.

And so when you look at it in that context

and then you back into what is NVIDIA's, what does NVIDIA

does NVIDIA do and how much of that

new economics, new industry would we have to benefit

to address, I think we're gonna be a lot, lot bigger.

And then the rest of it, to me, is is it

possible for NVIDIA to be a, you know, $3 trillion

revenues company in the near future? The answer is of course yes. And the

reason for that is because it's not limited by any physical limits.

There's nothing that I see that says, you know, gosh,

$3 trillion is not possible. And

as it turns out, NVIDIA's supply chain is, the burden is shared by 200 companies.

And the fact that we scale out on the backs with the partnership of this ecosystem,

the question is do we have the energy to do so?

And surely we will have the energy to do so. And so all of these things combined,

that number is just a number, you know? And I still remember, NVIDIA

was a, NVIDIA was a, the first time we crossed a billion dollars,

I was reminded of a CEO who told me, "You know, Jensen, it's theoretically

impossible for a fabless semiconductor company to exceed a billion dollars."

And I won't bore you with why, but the end,

of course it's illogical and there's a lot of evidence we're not. And then,

somebody told me, "You know, Jensen, you'll never be more than $25 billion

because of some other company." Somebody told me that, "You'll never be, you know,

because..." And so the, those aren't first principled thinking.

And the simple way to think about that is what is it that we make

and how large is the opportunity that we can create?

Now, NVIDIA is not in the market share business. Almost everything that I just talked

about don't exist. That's the part that's hard.

You know, if NVIDIA was a $10 billion

company trying to take market share, then it's easy to see

for shareholders that, oh, yeah, if they could just

take 10% share, they could be this much

larger. But it's hard for people to imagine how

large we could be because there's nobody I could take share from.

You know? And so I think that that's one of the

challenges.... for the world is, is the imagination of the

future. But I got plenty of time, and I'll keep reasoning about it, and I'll keep talking

about it, and every single GTC will become more and more real.

You know, and then more and more people will talk about it, and one of these days, you

know, we'll get there. But I'm 100% we'll get there.

- Yeah, this view of you know, token factories essentially, this token per

second per watt, and every token having value. Like

it's an actual thing that brings value, and it brings different kinds of value,

different amounts of value to different people with value. That's the actual product, is really

could be loosely thought of as the token. And so you have a bunch of token factories.

And then it's very easy, first principles, to imagine a future, given all

the potential things that AI can solve, that you're going to need an

exponential number more of token factories.

- And what's really interesting, the reason why I was so excited about it, the

iPhone of tokens arrived.

- What do you call it? Wait, are you saying OpenClaw's iPhone? That's interesting.

- Agents.

- Yeah, agents. True.

- Agents in general.

The iPhone of tokens arrived. It is the fastest-growing application in

history. It went straight up. Went straight up.

- That says something.

- Yep, there's no question OpenClaw is the iPhone of tokens.

- Is there something truly, as you know,

something truly special happening from about December,

where people have really woke up to the power of Claude Code of Codex, of OpenClaw.

Um, I mean, I'm embarrassed to admit that on the way here in the airport, I've ...

It's the first time I've done this in public. I was programming, quote unquote,

by talking to my laptop.

And I was embarrassed because I was pretending like I'm talking to a human colleague.

Uh, I'm not sure how I feel about the future where everybody-

... is walking around talking to their AI, but it's such an

efficient way to get stuff done.

- And it's more likely that your AI is bothering you all the time.

And the reason for that is because it's getting stuff done so fast.

It's reporting back to you, "I got that done."

"You know, what do you want me to do next?" You know, it... That's the part that I think-

... most people don't realize is they're The person who's gonna be chatting with

them, texting them most, is their claws or lobster.

- What an incredible future.

Uh, I read that you attribute a lot of your success to your ability to work harder than anyone

and withstand more suffering than anyone.

So we can list many of the things that entails. I mean, dealing with failure,

the cost and engineering problems we've talked about. The human

problems, uncertainty, responsibility, exhaustion, embarrassment,

the near-death company moments that you've mentioned,

But also the pressure. Now, as the CEO of this

company that economies and nations strategize around plan their,

Financial allocations around, plan their AI

infrastructure around, how do you deal with this much pressure?

What gives you strength, given how many nations and peoples depend on you?

- I'm conscious about the fact that,

NVIDIA's success is very important to United States.

We generate enormous amounts of tax revenues.

We established technology leadership for our nation. Technology

leadership is important for national security.

National security not just in one aspect of national security, all aspects

of national security. When our country's more prosperous,

we could do a better job with domestic policies and helping social benefits.

Because we're generating so much re-industrialization in the United States,

we're creating mountains of jobs. We're helping shift, how we,

how we build things back to the United States in so many

different plants, chips, computers, and of course, these AI

factories. I'm completely aware

that, that... And I have the benefit, and this is a real

a real gift with mainstream investors,

teachers, policemen who have somehow, for whatever reason,

invested in NVIDIA or because they watched Jim Cramer bought

some stock and now are millionaires. And I am completely aware of

that circumstance. I'm aware of the circumstance that NVIDIA,

is central to a very large network of

ecosystem partners behind us and downstream from us.

And so the way I deal with that is exactly what I just did. I reason about

what is... what is it that we're doing?

What is it causing? What's the impact that has on other people

benefit, you know, positively or even,

even through great burden, for example, to supply chain?

And the question is, therefore, what are you gonna do about it?

In almost everything that I feel, I break it down, I reason about, "Okay,

"what's the circumstance? What has changed? What's hard?

And what am I gonna do about it?" And I break it down, decompose the problem,

and the decomposition of these circumstances

turns it into manageable things that I can do. And the only thing after that

I could do is, "Did you do it? Did you either do it or did you

get somebody else to do it? And if you didn't do it,

you reasoned that you need to do it, and you didn't do it, and you didn't get anybody else to do it,

then stop crying about it." You know? And so- ... so I'm fairly

Tough on myself. And, but I also break things down so that,

so that I don't panic. I can go

to sleep because I've made the list of things that needed to be done, and

I've made sure that everything that could put our company in harm's way,

could put my partners in harm's way, put our industry in harm's way, I've told

somebody. Everything that I feel

could put anybody in harm's way, I've told someone. And I've told

that someone who could do something about it. And so I've gotten it off my chest

or I'm doing something about it. And so after that, Lex, what else can you do?

- So given all the insane, intense amount of suffering

on the journey of building up NVIDIA, have you hit low points psychologically?

- Oh, yeah. Oh, yeah. Sure. All the time. All the time.

- And there—

- All the time.

- ... you just break down the problem into pieces?

- Yeah. Yeah.

- See what you could do about it?

- And, and part of, and, you know, Lex, part of it, part of it is forgetting.

One of the most important attributes of AI learning, as you know, is,

right? Systematic forgetting. You, you need to know when to forget

some things. You can't memorize everything. You can't keep everything and, and,

you know, you, you want to— you don't want to carry everything. One of the things that I do very

quickly is decompose the problem, I reason about the problem, and I

share the load with it. When I say I tell everybody,

I'm essentially sharing that burden. As quickly as possible.

Whatever worries me, tell somebody else. Don't just keep it. You know,

don't freak them out. Decompose the problem into smaller parts and get

people to, so, and, and inspire them to be able to go do something about it.

But part of it is just, just forgetting. You know, like,

a lot of it is you gotta be tough on yourself. You know, just come on, stop

crying about it. Let's get going. You know? And, and then you get out of bed. And then

the other part is, is you, you're attracted to the

next shiny light, the next future, you know, the next opportunity, the

next, "Okay, that's behind us. Let— what's next?"

It's a lot, I think, you know, you watch this with great athletes. They,

they just worry about the next point.

The last point is behind them. The embarrassment, the, you know— the setback.

You know, and, and then, and because I do so much of my job publicly,

you know? Lex, you do a fair amount of your job publicly too. And so, so I do a lot of my

job publicly. And so you know, I say a lot of

things that, that seem sensible at the time or funny at the time,

mostly it's just because it's funny to me at the time. And then, you know, you

reflect on it, it's less funny, but, but...

- Yeah. No, trust me, I know. But you basically allow yourself to

be pulled by the light of the future. Forget the past and just keep-

- That's right.

- ... keep working towards that. I mean, you did say, there's this kind of

famous thing you said that if you knew how hard it would be

to build NVIDIA it turned out to be, what is it? A

million times more hard than you anticipated that you wouldn't do it.

- Yeah, right.

- Um, but isn't... You know, when I hear that,

that's probably true about everything worth doing, right?

- Exactly. That is, by the way, what I was trying to explain, is that there's

a, there's a incredible superpower of being, being have a, the mind of a child.

You know? And I say to myself oftentimes when I look at something,

and almost, almost everything, My first thought is, "How hard can it be?"

You know? And so you get yourself into that mode, how hard could

it be? And, and nobody's ever done it. It

looks gigantic. It's gonna cost hundreds of billions of dollars.

It's gonna take, you know, all this... And you just go, "Yeah, but how hard could it be?"

You know? How hard could it be?

And so you gotta get yourself into that state of mind. You don't

wanna, you don't wanna actually over simulate

everything and all the setbacks and all the trials and tribulations and all

the disappointments. You don't wanna simulate all that in advance. You don't wanna know that.

You don't, you wanna go into a new experience thinking it's gonna be perfect,

it's gonna be great, it's gonna be incredibly fun. And then while you're there,

you know, you need to have,

you need to have endurance, you need to have grit, so that when the setbacks

actually happened, and those setbacks are gonna surprise you, the

disappointments are gonna surprise you, you know, the embarrassments are

gonna surprise you, the humiliations are gonna surprise you.

You just can't let... Now you just gotta turn on the other bit, which is just forget

about it. Move on, keep moving. And, and to the extent that,

to the extent that my assumptions

about the future and why the future is gonna manifest,

so long as those assumptions and that input

doesn't change or didn't change materially, then I should

expect that the output won't change. And so my simulated output of the future

is still gonna happen. And if it's still gonna happen,

I'm still gonna go after it. I believe it's gonna, you know, and so there's a combination

of two or three human characteristics,

the ability to go into a, into an experience fresh-minded,

the ability to forget the setbacks, the ability to believe in yourself,

you know, to believe what you believe and stay, stay true to that belief.

But you're constantly reevaluating. This combination of three, four, five things

I think is, is really important for resilience. And,

and... and, you know, I'm fortunate that,

that whatever life experiences led to this, I've got kind of those

four, five things. You know, I'm always curious, always learning.

I'm always learning from everybody, you know? I'm always asking my...

And because I'm humble about everything, I'm always thinking,

"Gosh, they did that so nicely. They did that so wonderfully." You know,

I wonder what they're thinking through. How do they... You know, so I'm

simulating everybody. In a lot of ways, you know, I'm emulating almost everybody

I watch, right? You're empathetic towards everything that they

do that you're observing and respect. And, and so you're

constantly learning and, you know.

- You're now one of the wealthiest people on Earth. One of the

most successful humans on Earth.

Is it harder to be humble and to be able to... Do you feel the

effect of money and power and fame in making it harder for you to

sort of be wrong in your own head? Enough

to hear out an opinion of somebody else when they

disagree with you and learn from them? Those kinds of things.

- Um, surprisingly, no. And I would, I would actually go the other way.

Because I do so much of my work publicly, when I'm wrong,

pretty much everybody sees it.

- You get humbled. Fair enough.

- And when I'm wrong, when I'm wrong or it didn't turn out that

way or you know, I mean, most of the things that, that I say outside I'm fairly

certain about. And the reason for that is because, because it's gonna

impact somebody else and I want to be quite concerned about that and quite,

circumspect about that. For stuff that, that I'm reasoning about inside a meeting,

you know a lot of things could turn out differently. And

so, but it doesn't ever stop me from reasoning. The way that the way that I

manage and lead, I,

you know, I'm constantly reasoning in front of people. And even when I'm talking to you, you can

kind of see me kind of reasoning through things.

And I want to make sure that you understand what I'm saying not because I told you-

... because I'm so humble about what I'm about to tell you.

I kind of show you the steps that I got there.

And then you can decide whether you believe what I said in the end. And so I'm doing that

all day long in meetings.

With all of my employees, I'm constantly reasoning through, "Let me tell you what, how I

see it." And then I reason through it. It gives everybody the

opportunity to intercept and say, "I disagree with that part."

The nice thing about reasoning through things and letting people interact with

it is that they don't have to disagree with your outcome.

They can disagree with your reasoning steps.

And they could pull me in different directions, and then we can reason forward.

And so we're kind of, you know, collective

path searching method. And it's really fantastic.

- Yeah, you have this

way about you of ... When you're explaining stuff, I can feel you

actually reasoning on the spot about it with a

constant open-mindedness where you could ... I could feel like I could

steer your thinking.

And that's a, that's really beautiful that you've been able to maintain that after so many

years of success, and pain.

I think sometimes pain makes you close, closes you down a bit.

- Mm-hmm. Yeah.

- And I think to maintain-

- Yeah. Tolerance for embarrassment, I think is...

- Yes, that's ... The tolerance ... I mean, that's a real thing.

Is many years of embarrassing yourself. Even those meetings

knowing that there's people around you where you declared one idea and

it was shown that that idea was wrong-

... and be able to admit that and to grow from that. That's not, that's very difficult on a human

level.

- Yeah. Well, you know. They knew that recently my first

job was, you know, cleaning toilets, so.

- I'm glad you maintained that same spirit of Denny's

the, the work. I mean, that, that was beautiful. Your whole journey from, starting from Denny's is a

beautiful one. Let me ask you about video games. So I'm a big gaming fan.

So I have to say thank you to NVIDIA for many years of incredible graphics.

- By the way, GeForce is our still, to this day-

... our number one marketing strategy.

Right. People learn about NVIDIA while they're in their teenage years.

And then they go to college and they know who NVIDIA is and then in beginning

is just, you know, playing Call of Duty, you know? You know, Fortnite. And then later

they're using CUDA, and then later they're using NVIDIA and, you know, Blender and

Dassault and Autodesk.

- Yeah. I mean, I should say I mentioned to a friend that I'm

talking with you. He said, "Oh, they make great gaming GPUs."

- Yeah, exactly.

- It's like-

- Exactly.

- You know, there's more to it, but, yeah, yeah,

people really love the ... It really brought a lot of joy to a lot of people. The,

the, the hardware really brings these worlds to life.

There was some controversy around this with DLSS 5.

Can you explain to me the drama around this? Uh, I guess

people, the gamers online were concerned that it makes games look like AI slop.

Uh, what do you think of this drama?

- Yeah. Uh, I think their perspective makes sense

and I could see where they're coming from, because I don't love AI slop myself.

You know, all of the AI generated content increasingly,

um, looks similar and they're all beautiful,

and I can... So I can... I'm empathetic towards what they're thinking.

That's just not what DLSS 5 is trying to do. I showed several examples of it.

But DLSS 5 is

3D conditioned, 3D guided. It's ground truth structure data guided. And so the

artist determined the geometry. We are completely truthful.... to

the geometry maintains in every single frame.

It's conditioned by the textures, the artistry of the artist. And so every single

frame, it enhances but it doesn't change

anything. Now, the question is, the question about enhancing,

DLSS 5 also lets, because it's,

the system is open, you could train your own models to determine,

and you could even in the future prompt it. You know, I want it to be a toon

shader. I want it to look like this kinda, you know, so you can give it even

an example. And it would generate in the style of that,

all consistent with the artistry, you know, the

style, the intent of the artist. And so all of that is done

for the artist, so that they can create something that is more beautiful,

But still in the style that they want. I think that

they got the impression that the games are gonna

come out the way the games are shipped the way they do, and then

we're gonna post-process it. That's not what DLSS is intended to do.

DLSS is integrated with the artist, and so it's,

it's about giving the artist the tool of AI, the tool of

generative AI. They could decide not to use it, you know?

- I think people are very sensitive to human faces.

And we're now living in this moment, which I think is a beautiful one, which

is people are sensitive to AI slop.

It puts a mirror to ourselves to help us realize that what we seek is

imperfections. What we seek is sometimes not perfect graphics.

It helps us understand what we find compelling in the worlds we create.

And that's beautiful. And as long as it's tools that help us create those worlds-

- Yeah, that's right

- ... it's wonderful.

- That's right. Yet, yet another tool, and they want the generative,

models to generate the opposite of photo real.

Yeah, it'll do that too. And so it's just yet another tool. I think the

the gamers might also appreciate that, that in the last couple of years,

we introduced skin shaders

to the game developers. And many of those games have skin shaders

that include subsurface scattering that make skin look more skin-like. And so the

industries, you know, game developers are looking for more and more and more

tools to express their art. And so this

is just yet more, one more tool, and they get to decide what to use.

- Ridiculous question. What do you think is the greatest or most influential

game ever made? Maybe from NVIDIA's perspective?

- Doom.

- Doom, unquestionably. That was the start of the 3D.

- I would say Doom, from an art, the intersection of the

cultural implication as well as the industry, turning a PC into a gaming device.

That was a very important moment. Now, now of course, flight simulation companies were

before it. And but they just didn't have the popularity that Doom did to have made

the industry turn the PC from an office automation tool into a personal computer for

families and gamers and things like that. And so Doom was really impactful there.

From an actual game technology perspective, I would say Virtua Fighter.

And so we're great friends with both of them, you know?

- And then there's games more recently, I mean, Cyberpunk 2077,

really nice GPU-accelerated graphics. Like-

- Fully ray traced.

- Fully ray traced. Also, I like, I personally, I'm a huge fan of Skyrim,

uh, Elder Scrolls, and the, you know, it's, it's been released a long, long time

ago, but people release mods and-

- We love mods

- ... they create these, these inc- I mean, it- ... it's like a different game and it just

allows me to replay the game over and over and get i-

It makes you realize that you can re- experience in a

totally new way the world you already love. So-

- That's right

- ... I do that all the time. One of my favorite things is just walk across Skyrim.

- Uh, we created this thing called RTX Mod. Yeah, it's a modding tool.

- Awesome.

- It allows the community to inject the latest technology into an old game.

- Of course, like what makes a great video game is not just graphics, it's also story and

character development, but-

- That's right

- ... beautiful graphics can add to the immersion.

The feeling like it's another place you're transported to.

Ah, what you said, I think accurately, that the AGI timeline

question rests on your definition of AGI.

So, let me ask you about possible timelines here.

Let's, this ridiculous definition perhaps of what AGI is,

but an AI system that's able to essentially do your job. So, run, no,

start, grow, and run a successful technology company that's worth-

- A good one or a one?

- No. It has to- It has to be worth more than a billion,

more, more than a billion dollars. So,

you know, you know how hard it is to do all those components.

So, how far are we away from that? So, we're

talking about OpenClaw that does all the

incredibly complex stuff that are required to

to, first of all, innovate, to find customers, to sell to them, to

manage, to build a team of

some agents, some humans, all that kind of stuff. Is this five, 10,

15, 20 years away?

- I think it's now. I think we've achieved AGI.

- Do you think you could have a company run by an AI system like this?

- Possible, and the reason for that is this. You said a billion, and you didn't

say forever. And so for example, uh... It is not out of the question

that a Claw was able to create a web service, some interesting little app that

all of a sudden, you know, a few billion people used for 50 cents, and then

it went out of business again shortly after. Now, we saw a whole bunch of those type of companies during the

internet era, and most of those websites were not anything more

sophisticated than what OpenClaw could generate today.

- Interesting. Achieve virality and monetize that virality.

- Yeah. It's just that I don't know what it is, but I couldn't have predicted any of

those companies at the time either, you know? And -

- You're gonna get a lot of people excited with that statement.

It's like, what do you mean? I can- I can just, uh - ... launch an agent and

make a lot of money.

- Well, by the way, it's happening right now, right? You know that when, when you go to China

you're gonna see, you're gonna see a whole bunch of people

teaching their, getting their Claws to try to go out and look for jobs and, you know,

do work, make money.

And I'm not, I'm not actually... I wouldn't be surprised if some

social thing happened or somebody created a, a

digital influencer, super, super cute or

some social application that, you know, feeds your little

Tamagotchi or something like that, and, and it become an out of the

blue an instant success. A lot of people use it

for a couple of months and it kind of dies away. Now, the odds of,

you know, 100,000 of those agents, Building NVIDIA is zero percent.

And then the one part that I will, I won't do,

And I, I want to make sure we all do, is to

recognize that people are really worried about their jobs.

And I just want to remind them that the purpose of your job

and the tasks and tools that you use to do your

job are related, not the same. I've been doing my job for

33 years. I'm the longest running tech CEO in the world, 34 years.

And the tools that I've used to do my job have changed

continuously in the last 34 years, and sometimes quite

dramatically, you know, over the course of a couple, two, three

years. And the one story that I really

wanna make sure that everybody hears is the story that the

first job that computer scientists said, AI

researchers said was gonna go away was radiology.

Because computer vision was going to achieve superhuman levels, and it did.

CV... Computer vision was superhuman in 2019, 20, maybe a little bit later, 2020?

Okay? And so it's been a long time since computer vision has been superhuman. And

so the prediction was radiologists would go away because studying

radiology scans was a thing of the past. AI will do that. Well,

they were absolutely right.

Computer vision is completely superhuman. Every radiology

platform and package today is driven by AI,

and yet the number of radiologists grew. And so

the question is why? And we now have a shortage of radiologists in the world.

And so, one, the alarmist warning went too far and it scared people from

doing this profession that is so important to society.

And so it did harm. Now, why was it wrong? The reason why is because

the purpose of a radiologist, the purpose is to diagnose disease

and help patients and doctors diagnose disease.

And because we're able to study scans at so much faster

now, you could study more scans, you could diagnose better, you could,

you could inpatient faster,

you can see people more. The hospitals are making more money. You have

more patients in the hospital. You need more radiologists. I mean, the

amazing thing is, it's so obvious this was gonna happen.

The number of software engineers at NVIDIA is gonna grow, not decline.

And the reason for that is because the purpose of a software engineer

and the task of a software engineer coding are related, not the same.

I wanted my software engineers to solve problems. I didn't care how many lines of code they

wrote, you know? But their job, their purpose of their job didn't change. Solving

problems, working as a team, diagnosing problems, evaluating the result,

looking for new problems to solve, innovation, connecting

dots. You know, none of that stuff is gonna go away.

- Do you think it's possible that... Let's even take coding. Do you think the number of

programmers in the world might increase, not decrease?

- Yes. And the reason for that is this. What is the definition of coding?

I believe it is... The definition of coding, as of today, is simply specifying,

specification, and maybe if you want to be

rather directive, you could even give it an architecture of the software

that you wanted to write. So the question is, how many people could do that?

Describe a specification for a computer to go... Telling the

computer what to go build. How many people? I think we just went from 30

million to probably 1 billion. And so every carpenter in the future will be

a coder, except a carpenter with AI is also an architect.

They've just increased the value that they could deliver to the customer. Their,

their artistry just elevated tremendously.

I believe that every accountant is, you know, also your financial analyst,

also your financial advisor. So, all of these professions have

just been elevated.... and, and if I were a carpenter, I sees a,

I see AI, I would just completely go berserk.

You know, the services I can bring to my clients if I were a plumber,

completely go berserk.

- And the people that are currently programmers and software engineers, I think

they're at the cutting edge of understanding intuitively how to communicate

with the agents using natural language in order to design the best kind of software.

- That's right, exactly.

- So over time they'll converge, but I think

there's still value in getting, I think learning how to program,

like learning what programming languages are. The old, the old kind of

programming, what are good practices for

programming languages, what are design principles for programming-

- That's right

- ... Languages for large software systems?

- And the reason for that,

Lex, and you know, as you're saying for the audience, I think

the goal of, the goal of specification, the artistry of

specification, the goal and the artistry of it,

Is going to depend on what problem you're trying to solve.

When I'm thinking, when I'm thinking about giving the company strategies and

formulating corporate directions and things that we should do,

I describe it at a level that is sufficiently

specific that people generally understand the direction

and it's actionable. It's specific enough that they can take action on it,

but I under specify it on purpose, so that

enable 43,000 amazing people to make it even better than I imagined.

And so when I'm working with engineers and when I'm working with people,

I think about who, what problem am I trying to solve? Who am I working with?

And the level of specification, the level of architecture definition

relates to that. And, and so

everybody's going to have to learn how, where in the spectrum of coding they want

to be. Writing a specification is coding.

And so you might decide to be quite prescriptive because there's a very

specific outcome you're looking for. You might decide that, you know,

this is an area you want to be much more exploratory, and so you might under

specify and enable you to go back and forth with the

AI to even push your own boundaries of creativity.

And so this artistry of where you are in the spectrum, this is the future of coding.

- But just to linger on it outside of coding, I think a lot of people, rightfully so,

are worried about their jobs, have a lot of anxiety about their jobs, especially in

the white-collar sector. I don't think any of us know what to do,

With tumultuous times that always come when automations and new technology

arrives. And I just... First of all, I think

we all need to have compassion and the responsibility to feel sort of the burden

of what the actual suffering feels like for individual people and families that lose their

job. I think whenever you have transformative technology

like that's coming with artificial intelligence, there's going to be a lot of pain,

and I don't know what to do about that pain. Hopefully, it creates much

more opportunities for those same people, for the same kind of job as the tooling

evolves and makes them more productive and makes them more fun,

hopefully, as it does in the programming. I've been having so much fun programming,

I have to say. Like, I've never had this much fun. So hopefully it makes their job,

automates the boring parts and makes the creative parts,

the ones that the human beings are responsible for. But still there's going to be

a lot of pain and suffering.

- So my first recommendation before... And this is now how I deal

with anxiety. In fact, we just talked about it earlier.

Enormous anxiety about the future, enormous anxiety about the pressure, enormous

anxiety about uncertainty, I first break it down, and then I'm gonna tell myself,

"Okay, there are some things you can do something about, there's some things you can't do anything

about. But for the stuff that you can do something about, let's reason

about it and let's go do it."

If we were to hire a new college graduate today, and I have a choice between two,

one that have, that is no clue what AI is

and one that is expert in using AI, I would hire the one who's expert in using AI.

If I had an accountant, a marketing person,

the one that is expert in using AI, supply chain,

customer service, a salesperson, business development, a lawyer,

I would hire the one who is expert in using AI.

And so I would advise that every college student,

every teacher should encourage their student to be, to go use

AI. Every college student should graduate and be an

expert in AI. And everybody, if you're a carpenter, if

you're, you know, electrician, go use AI.

Go see what it can do to transform your current job,

elevate yourself. If I were a farmer, I would absolutely use AI.

If I were a pharmacist, I would use AI. I wanna see how, what it

could do to elevate my job so that I could be the innovator to

revolutionize this industry myself.

And so that would be the first thing that I would do. And then I would

also, I would also help them...

it is the case that the technology will dislocate and will eliminate many tasks

if... And because it will automate it, if your job is the

If your job is the task, then you're very highly going to be disrupted.

If your job's purpose includes you, certain tasks-

... then it's vital that you go learn how to use AI to automate those tasks.

And then there's the world of spectrum in between.

- And by the way, the beautiful thing about AI, so the chatbot versions,

is you can break down... You have anxiety

and you can break down the problem by talking to it.

Like, I've recently... It's really just incredible how much you can think

through your life's problems, and through... And I don't mean, like, therapy problems.

I mean, like, very practically, "Okay, I'm worried about my..." Literally,

"I'm worried about my job. What are the skills? What are the steps I need to take? How do I get better at

AI?" Everything you just said, you could literally ask and it's going to give you-

... a point-by-point plan. I mean, it's just a great life coach, period. This-

- I don't know how to use AI, and the AI goes, "Well, let me show you."

- Exactly. It's very meta, but it's-

It's kind of incredible. So people definitely should-

- You can't walk up to Excel and say, "I don't know how to use Excel." You're done.

- I mean, that's really what AI has done for me in all walks of life,

is that initial friction of being a beginner of using a thing for the first time.

I can literally ask about any single thing,

"What are the first steps I need to take?"

- That's right.

- And that handholding that it does, removing the friction

of all the experiences that the world offers is... You

know, like I mentioned to you offline, you mentioned, "I'm going to China

and Taiwan."

- So awesome.

- Just ask, "Where do I-"

- So excited for you.

- "Where do I—what do—" "You know, where do I go? How do I..." All of those questions—

... immediately answered, and it's beautiful.

- Well, when you, when you go to Taiwan, just ask AI- ... "What are

Jensen's favorite restaurants in Taiwan?" And it'll actually-

- You don't know?

- Oh, yeah.

- Is it accurate? Okay.

- Yeah.

- All right.

- It's all over Taiwan.

- Well, you're a rockstar over there. And like we also mentioned

offline, maybe our paths will cross, which would be really wonderful in computing.

- COMPUTEX. NVIDIA GTC Taiwan.

- Uh, do you think there's some things about human nature, about human consciousness

that is fundamentally non-computational? Maybe something a chip, no matter

how powerful, can never replicate?

- I don't know if the chip will ever get nervous. And that's the, you know, of course,

the conditions by which that causes anxiety or nervousness or whatever emotion. Um,

I believe that AI will be able to recognize those

and understand those. I don't think my chips will feel those.

And therefore, the... How that anxiety, how that

feeling, how that excitement, how that, how that, you know...

All of those feelings manifest in human performance. For example,

extremely amazing human performance, athletic performance, you know,

average or lesser than average. That entire

spectrum of human performance that comes out of

exactly the same circumstances for different people,

manifesting a different outcome, manifesting a different performance. I don't think

there's anything about anything that we're building that would suggest

that two different computers

being presented with all of exactly the same context would perfo-

Of course, it would produce statistically different outcomes, but it's not because

it felt different.

- Yeah, the subjective...

Boy, there's something truly special about the subjective experience

that we humans feel. Like I mentioned to you, I was pretty nervous talking to you.

Like I mentioned to you, that, the hope, the fear, the anxiety,

and just life itself, the richness of life. How amazing everything is. How

deeply we fall in love, how deeply our hearts get broken,

how afraid we are of death and how much pain we feel when our

loved ones pass away. All of that, the whole thing. I know it's very hard to-

... think AI being able to... A computational device being able to do that.

But there's so many mysteries about this whole thing that we're yet to

uncover, that I am open to be surprised. I've been surprised a lot over the past-

... few months and few years. Scaling can create some

incredible miracles in the space of intelligence.

Has been truly marvelous to watch, so I'm open to surprise.

- And it's just really important to break down what is

intelligence. You know, the word, that word we use all the time, it's

not a mysterious word. Intelligence has a meaning, you know?

And it's a system that... You know, it's something that we

do that includes perception and understanding and

reasoning and the ability to do plan. And, you know, that, that

loop, that loop, is the... Fundamentally what intelligence is.

Intelligence is not one word that is exactly equal to humanity.

And that's, I think it's really important to separate the two. We have two words for

that. I'm not... I don't over-fantasize about, and I don't

over-romanticize about intelligence. Intelligence

is... And people have heard me say it before, I actually think

intelligence is a commodity. I'm surrounded by intelligent people.

And I'm surrounded by intelligent people more intelligent than I am in each one of the

spaces that they're in. And yet,

I have a role in that circle. It's actually kind of interesting.

They're more educated than I am. They went to better schools than I did.

They're deeper than, in any of the fields that they're in. All of 'em.

I have 60 of 'em. They're all superhuman to me.

And somehow, I'm sitting in the middle orchestrating all 60 of 'em.

And so you gotta ask yourself... Uh, what is, what is

it about a dishwasher that allows that

dishwasher to sit in the middle of superhumans? Does that make sense?

And so, but that's my point. My point is intelligence is a functional thing.

Humanity is not a, not specified functionally. It's a much, much bigger word.

And, and our life experience, our tolerance for

pain, our determination, those are, those are different words than intelligence.

And so the thing that I wanna help the audience understand,

if I could give them one thing, is, is

intelligence is a word that we've elevated to a very high form over time.

- The, the word we should really elevate is humanity.

- Character, humanity.

- All those things.

- All of those things. Compassion, generosity,

all of the things that you said just now,

I believe those are superhuman powers. And that now

intelligence is gonna be commoditized.

Because we've spoken about it, the most important thing is your education.